Abductive Reasoning Task

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An Abductive Reasoning Task is a non-monotonic reasoning task that requires an abductive argument.



  • (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Abductive_reasoning Retrieved:2019-9-1.
    • Abductive reasoning (also called abduction,[1] abductive inference,[1] or retroduction ) is a form of logical inference which starts with an observation or set of observations then seeks to find the simplest and most likely explanation for the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. Abductive conclusions are thus qualified as having a remnant of uncertainty or doubt, which is expressed in retreat terms such as "best available" or "most likely." One can understand abductive reasoning as inference to the best explanation, although not all usages of the terms abduction and inference to the best explanation are exactly equivalent. In the 1990s, as computing power grew, the fields of law, [2] computer science, and artificial intelligence research [3] spurred renewed interest in the subject of abduction.

      Diagnostic expert systems frequently employ abduction.

  1. 1.0 1.1 For example:
  2. See, e.g. Analysis of Evidence, 2d ed. by Terence Anderson (Cambridge University Press, 2005)
  3. For examples, see "Abductive Inference in Reasoning and Perception", John R. Josephson, Laboratory for Artificial Intelligence Research, Ohio State University, and Abduction, Reason, and Science. Processes of Discovery and Explanation by Lorenzo Magnani (Kluwer Academic/Plenum Publishers, New York, 2001).